中文摘要：

服务匹配是服务发现的主要环节.目前,原子服务匹配过程主要存在服务匹配概念狭窄、匹配算法的时间复杂度较高及匹配方案的表示难以被智能优化算法处理等问题.针对上述问题,在原子服务匹配的基础上引入复合服务匹配、抽象复合服务匹配过程的适应度函数及约束条件,设计适用于智能优化算法处理的匹配方案的表示方法.同时,结合协同演化算法设计思路,提出基于粒子群和模拟退火的协同演化算法（PSO-SA）,用以求解复合服务匹配.实验结果表明：与现有智能优化算法相比,PSO-SA可在有限迭代次数内获得精度较高的匹配结果,对不同维度的服务匹配问题具有较高的适应性,可用于提高服务发现结果的质量.

英文摘要：

Service matching is a principal process of Web services discovery. Nowadays, the narrow concept of the atomic Web service matching, the high time complexity of the current matching algorithm and the difficult expression of the Web service matching for the intelligent optimization algorithms become the main problems in Web service matching development. To solve the above problems, this article introduces the concept of the compound service matching by extending the concept of the atomic service matching, and abstracts the mathematical expression of the compound matching problem by the fitness function and restriction. The expression of the solution of the Web service matching for the intelligent optimization algorithm is also proposed. Based on the co-evolutionary idea of particle swarm optimization （PSO） and simulated annealing （SA）, the study puts forward a co-evolutionary algorithm （PS0-SA） to the compound Web service matching problem. According to the experimental results, PSO-SA achieves better matching precision than other optimization algorithms within the limit iterations on various dimensional matching problems. Also, PSO-SA shows the adaptive ability to the compound service matching and improves the quality of result of Web services discovery.